Developing a model of participation post-stroke: a mixed-methods approach
Bibliographic record
Abstract
PURPOSE: To develop and verify a model of participation post stroke, as a domain of health related quality of life. METHODS: An explanatory sequential mixed-methods design was selected. The quantitative phase developed a participation model with data from an observational study of 453 participants at 12 months post-stroke, using structural equation modeling. The qualitative phase followed to verify the model from the perspective of individuals post-stroke. Data was collected from two groups of eight stroke survivors involved in a multicentre trial about participation in the community. Individuals took photographs to describe participation; discussion of photographs was conducted over the course of three focus groups, and analysis identified emergent categories related to the model. RESULTS: The 12-month post stroke participation model consisted of latent variables: accomplishment, restricted roles, and health efficacy. The model fit was reasonable: normed χ(2) = 2.95, RMSEA = 0.066(0.052; 0.079). The qualitative data verified the model; participants initiated photo-taking and discussion of all aspects of the model. Concepts of social support, environment, and cognitive difficulties were also discussed in relation to participation. CONCLUSIONS: The participation model was developed and verified. Additions to future models are suggested. Theoretical, clinical, and research implications are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".